Insight / Blog
Smart Water Treatment AI Operations : Early Equipment Diagnostics
Water treatment plants are critical infrastructure that must manage water quality, energy use, and equipment condition at the same time. Operators use field data to decide chemical dosing and pump schedules, and abnormal conditions require fast, consistent decisions.
A regional water utility analyzed key signals from its treatment facilities to diagnose operating conditions in real time and identify early signs of equipment anomalies.
Background : Coordinating Process Data and Equipment Signals
Water treatment operations cannot be optimized through equipment automation alone. Water quality, flow changes, chemical dosing, and pump operating patterns must be considered together.
Operators previously had to review increasingly complex process and equipment signals manually before making decisions. The utility needed an AI-assisted diagnostic framework that could surface anomalies earlier and support operating judgment.
Approach : Early Anomaly Detection from Sound, Vibration, and Current
SURROMIND built a signal-analytics framework that can extend across industrial sites where high-pressure pumps and large motors operate continuously.
- Core equipment diagnosis: focused on sound, vibration, and electrical-current signals from pumps and motors
- Process-data linkage: matched pump operating patterns with signals generated by the treatment process
- Decision support: validated AI models with field measurements to identify early signs of anomalies
Results : 100% AI Failure-Prediction Accuracy and Cost-Saving Potential
| Area | Validated outcome |
|---|---|
| Chemical use | Potential reduction of approximately 4-10% |
| Chemical-process cost | Potential savings of up to KRW 10 million per year |
| Pump electricity cost | Potential savings of approximately KRW 149-210 million per year with EMS-optimized operation |
| Predictive maintenance | 100% AI fault-prediction accuracy validated under the project's PMS conditions |
Operational Impact : Preventive-Maintenance Decisions for 24/7 Equipment
The purpose of a smart water treatment plant is not to replace operators. It is to help them interpret complex equipment signals more quickly.
SURROMIND focused on vibration patterns and electrical signals from large pumps and motors that operate around the clock in water utilities and other industrial facilities.
The AI models tracked time-series signals as operating conditions changed.
Equipment may appear to operate normally even as vibration or current patterns shift gradually with wear.
Detecting these changes helps operators assess equipment wear and failure risk earlier.
By analyzing signal changes, the system can flag conditions that may lead to shutdowns and support root-cause review and preventive-maintenance decisions.
